Book Overview & Buying
Table Of Contents

Training Systems Using Python Statistical Modeling

By : Miller

1 (1)

Buy this Book

Training Systems Using Python Statistical Modeling

1 (1)

By: Miller

Buy this Book

Overview of this book

Python's ease-of-use and multi-purpose nature has made it one of the most popular tools for data scientists and machine learning developers. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book is designed to guide you through using these libraries to implement effective statistical models for predictive analytics. You’ll start by delving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will focus on supervised learning, which will help you explore the principles of machine learning and train different machine learning models from scratch. Next, you will work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. The book will also cover algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. In later chapters, you will learn how neural networks can be trained and deployed for more accurate predictions, and understand which Python libraries can be used to implement them. By the end of this book, you will have the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Classical Statistical Analysis

Technical requirements

Computing descriptive statistics

Classical inference for proportions

Classical inference for means

Diving into Bayesian analysis

Bayesian analysis for proportions

Bayesian analysis for means

Finding correlations

Summary

Introduction to Supervised Learning

Principles of machine learning

Training models

Evaluating models

Summary

Binary Prediction Models

K-nearest neighbors classifier

Decision trees

Random forests

Naive Bayes classifier

Support vector machines

Logistic regression

Extending beyond binary classifiers

Summary

Regression Analysis and How to Use It

Linear models

Evaluating linear models

Bayesian linear models

Ridge regression

LASSO regression

Spline interpolation

Summary

Neural Networks

An introduction to perceptrons

Neural networks

MLPs for classification

MLP for regression

Summary

Clustering Techniques

Introduction to clustering

Exploring the k-means algorithm

Evaluating clusters

Hierarchical clustering

Spectral clustering

Summary

Dimensionality Reduction

Introducing dimensionality reduction

Principal component analysis

Singular value decomposition

Low-dimensional representation

Summary

Other Books You May Enjoy

Leave a review - let other readers know what you think

Linear models

Let's start by discussing the basics of linear models. In this section, we will examine the objectives of regression, what a linear model is, and how to evaluate the quality of a fit. We will explore estimation with the help of OLS. Additionally, we will take a look at the OLS method of estimation in action by trying to predict the price of homes in Boston.

Classification and regression are both methods of prediction, but they each predict variables of different natures. For instance, classification predicts whether a data point belongs to one of a finite number of classes, such as whether a viewer clicks on an ad on a web page or not. On the other hand, regression predicts a value from a continuum, such as how much a customer will spend on a website. The following diagram demonstrates the difference between classification and regression: